Price: $1,999.00

Length: 2 Days
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Monte Carlo Simulation Training

monte-carlo

Monte Carlo simulation is a computerized mathematical technique to generate random sample data based on some known distribution for numerical experiments. This method is applied to risk quantitative analysis and decision making problems.

This method is used by the professionals of various profiles such as finance, project management, energy, manufacturing, engineering, research & development, insurance, oil & gas, transportation, etc.

In engineering, Monte Carlo simulation involves using random number generators to simulate random effects. Simulating an event many times allows us to measure the variation just as we would if we took many samples of a real event.

Generally quite large simulations are required to give stable results. Monte Carlo simulation is an extremely useful and versatile technique for understanding variation in manufacturing processes and uncertainty in measurements.

The basic idea behind the Monte Carlo Simulation is a multiple random sampling from a given set of probability distributions. These can be of any type: normal, continuous, triangular, Beta, Gamma – you name it.

To use this technique, you need to follow specific steps, such as:

  • Identify all input components of the process and how do they interact e.g., do they sum up or subtract?
  • Define parameters of the distributions
  • Sample from each of the distributions and integrate the results
  • Repeat the process as many times as you want

Monte Carlo simulation furnishes organizations with a range of possible outcomes and the probabilities they will occur for any choice of action. For example, Monte Carlo simulation will elucidate:

  • The extreme possibilities
  • The outcomes of going for broke and for the most conservative decision
  • All possible consequences for middle-of-the-road decisions

Since its introduction in World War II, Monte Carlo simulation has been used to model a variety of physical and conceptual systems.

By using probability distributions for uncertain inputs, you can represent the different possible values for these variables, along with their likelihood of occurrence. Probability distributions are a much more realistic way of describing uncertainty in variables of a risk analysis, making Monte Carlo simulation far superior to common best guess or best/worst/most likely analyses.

Monte Carlo Simulation Training by Tonex

Monte Carlo Simulation Training is a 2-day presenting two types of Monte Carlo simulations.

Monte Carlo simulation is a method for performing calculations when you have uncertainty about the inputs.  Monte Carlo simulation is a technique used to understand the impact of risk and uncertainty in engineering projects, project management, cost, and other forecasting models.

Risk analysis is part of every decision we make, and we must face uncertainty, ambiguity, and variability. Monte Carlo simulation or Method allows us to see all the possible outcomes of your decisions and assess the impact of risk, allowing for better decision making under uncertainty.

Monte Carlo Simulation Training course introduces fundamental issues in simulation-based analysis and Monte Carlo-based computing.  Participants will learn about rigorous analysis and interpretation and an objective treatment of various approaches.

Course Objectives

After the completion of this course, the students will be able to:

  • Learn the fundamentals of modeling and simulation (M&S)
  • Review and discuss principles and algorithms for simulation
  • Describe the key concepts and terminology of Monte Carlo Simulation
  • Review Monte Carlo Simulation Methods
  • Learn about Limitations and Assumptions with methods in simulation and Monte Carlo
  • Apply Monte Carlo Simulation to specific project
  • Describe key real-world application of Monte Carlo Simulation Methods
  • Describe principles and theory of Monte Carlo Simulation Methods for systems, systems of systems (SoS), capabilities and systems engineering
  • Gain insight into complex systems, capabilities and difficult problems through use of Monte Carlo Simulation Methods
  • Discuss Monte Carlo Simulation Methods and techniques for combat, Electronic Warfare, threats and combat, and cybersecurity
  • Discuss Monte Carlo Simulation Methods and techniques for other domains such as aerospace, deep space, transportation, functional safety, manufacturing, power and energy, cyber security, health care, training/education, weather forecasting, infrastructure, and testing

Course Topics

Introduction to Monte Carlo Simulation

  • What is Monte Carlo Simulation?
  • How does Monte Carlo Simulation Work?
  • Risk Analysis 101
  • Statistics, Probability and Forecasting 101
  • Monte Carlo Simulation Applied Domains
  • Monte Carlo Simulation vs. Deterministic, or “single-point estimate” Analysis
  • Advantages and Disadvantages of Various Methods

Principles of Monte Carlo Simulation Method

  • Monte Carlo Simulation as a Computerized Mathematical Technique
  • Risk in Quantitative Analysis and Decision Making
  • Possible Outcomes and the Probabilities
  • Estimating Ranges of Values
  • Basic Forecasting Model
  • Forecasting Model Using Range Estimates
  • Model building
  • Bias-variance Tradeoff
  • Model Selection
  • Fisher information Matrix
  • Model Fitting
  • Random Number Generation
  • Stochastic Simulations
  • Simultaneous Perturbation Stochastic Approximation (SPSA) Algorithm
  • Simulation-based Optimization by Gradient-free Methods
  • Finite difference stochastic approximation (FDSA)
  • Markov chain Monte Carlo (MCMC)

Forecasting with Monte Carlo Simulation

  • Probability of Completion Within Specified Time
  • Extreme Possibilities
  • Principles of Probability Distribution
  • Using probability distributions, variables
  • Common probability distributions
  • Normal Or “bell curve”
  • Lognormal
  • Uniform
  • Triangular
  • PERT
  • Discrete

Monte Carlo Simulation Accuracy

  • Probabilistic Results
  • Sensitivity Analysis
  • Distribution Models
  • Uniform Distribution
  • Discrete Distribution
  • Normal Distribution
  • Triangular Distribution
  • Beta-PERT Distribution

Case Studies

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